Systematic Construction and Validation of an RNA Binding Proteins-Based Signature for Prognostic Prediction in Gastric Cancer

Abstract Background: Gastric cancer (GC) is one of the most common cancers with high incidence and mortality worldwide. Recently, RNA-binding proteins (RBPs) have drawn more and more attention for its role in cancer pathophysiology. In this study, we aim to explore the function and clinical implication of RBPs in GC. Methods: RNA sequencing data along with the corresponding clinical information of GC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed RNA-binding proteins (DERBPs) between tumor and normal tissues were identified by ‘limma’ package. Functional enrichment analysis and the protein-protein interaction (PPI) network were harnessed to explore the function and interaction of DERBPs. Next, Univariate and multiple Cox regression were applied to screen prognosis-related hub RBPs and to construct a signature for BC. Meanwhile, a nomogram was built based on the same RBPs. Results: A total of 296 DERBPs were found, and most of them mainly related to post-transcriptional regulation of RNA and ribonucleoprotein. A PPI network of DERBPs was constructed, consisting of 262 nodes and 2567 edges. A prognostic signature was built depended on seven prognosis-related hub RBPs that could divide GC patients into high- and low-risk groups. Survival analysis showed that the high-risk group had a worse prognosis compared to the low-risk group and the time-dependent receiver operating characteristic (ROC) curves suggested that the signature existed moderate predictive capacities of survival for GC patients. Similar results were obtained from another independent set GSE84437, confirming the robustness of signature. Calibration plots reported good consistency between overall survival (OS) prediction by nomogram and actual observation. Conclusion: The findings of this study would provide evidence of the effect of RBPs on GC as well as offering novel potential biomarkers in prognosis prediction and clinical decision for GC patients.